Back to Search
Start Over
A semi-automated method for unbiased alveolar morphometry: Validation in a bronchopulmonary dysplasia model
- Source :
- PLoS ONE, PLoS One (print), 15(9):e0239562. Public Library of Science, PLoS ONE, Vol 15, Iss 9, p e0239562 (2020)
- Publication Year :
- 2020
- Publisher :
- Public Library of Science, 2020.
-
Abstract
- Reproducible and unbiased methods to quantify alveolar structure are important for research on many lung diseases. However, manually estimating alveolar structure through stereology is time consuming and inter-observer variability is high. The objective of this work was to develop and validate a fast, reproducible and accurate (semi-)automatic alternative. A FIJI-macro was designed that automatically segments lung images to binary masks, and counts the number of test points falling on tissue and the number of intersections of the air-tissue interface with a set of test lines. Manual selection remains necessary for the recognition of non-parenchymal tissue and alveolar exudates. Volume density of alveolar septa ([Formula: see text]) and mean linear intercept of the airspaces (Lm) as measured by the macro were compared to theoretical values for 11 artificial test images and to manually counted values for 17 lungs slides using linear regression and Bland-Altman plots. Inter-observer agreement between 3 observers, measuring 8 lungs both manually and automatically, was assessed using intraclass correlation coefficients (ICC). [Formula: see text] and Lm measured by the macro closely approached theoretical values for artificial test images (R2 of 0.9750 and 0.9573 and bias of 0.34% and 8.7%). The macro data in lungs were slightly higher for [Formula: see text] and slightly lower for Lm in comparison to manually counted values (R2 of 0.8262 and 0.8288 and bias of -6.0% and 12.1%). Visually, semi-automatic segmentation was accurate. Most importantly, manually counted [Formula: see text] and Lm had only moderate to good inter-observer agreement (ICC 0.859 and 0.643), but agreements were excellent for semi-automatically counted values (ICC 0.956 and 0.900). This semi-automatic method provides accurate and highly reproducible alveolar morphometry results. Future efforts should focus on refining methods for automatic detection of non-parenchymal tissue or exudates, and for assessment of lung structure on 3D reconstructions of lungs scanned with microCT. ispartof: PLOS ONE vol:15 issue:9 ispartof: location:United States status: published
- Subjects :
- 0301 basic medicine
Pulmonology
Intraclass correlation
Stereology
Diagnostic Radiology
0302 clinical medicine
Pregnancy
Medicine and Health Sciences
Segmentation
Mathematics
Bronchopulmonary Dysplasia
Mammals
Observer Variation
Multidisciplinary
Radiology and Imaging
Histological Techniques
Software Engineering
Eukaryota
Animal Models
Research Assessment
Pulmonary Imaging
Reproducibility
Lung structure
Experimental Organism Systems
Vertebrates
Leporids
Medicine
Engineering and Technology
Radiographic Image Interpretation, Computer-Assisted
Female
Rabbits
Automated method
Research Article
Computer and Information Sciences
Imaging Techniques
Science
Chronic Obstructive Pulmonary Disease
Hyperoxia
Research and Analysis Methods
Volume density
Computer Software
03 medical and health sciences
Signs and Symptoms
Diagnostic Medicine
Linear regression
Image Interpretation, Computer-Assisted
Animals
Emphysema
business.industry
Morphometry
Organisms
Biology and Life Sciences
Pattern recognition
X-Ray Microtomography
Fibrosis
Pulmonary Alveoli
Disease Models, Animal
030104 developmental biology
Pulmonary imaging
030228 respiratory system
Amniotes
Animal Studies
Artificial intelligence
Clinical Medicine
business
Zoology
Developmental Biology
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Volume :
- 15
- Issue :
- 9
- Database :
- OpenAIRE
- Journal :
- PLoS ONE
- Accession number :
- edsair.doi.dedup.....14742d6010889efa266ffaa32480ec1e